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for satisfaction and motivation. We create a work-life-balance through flexible working time models and the possibility of mobile working, because creative and innovative ideas can arise anywhere. We also attach
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Implementation, further development and adaptation of AI models and research prototypes for the annotation and scoring of text data, in particular for the optimization of AI-supported teaching and learning in
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Astronomy. Main projects include eROSITA, ATHENA, H.E.S.S., CTAO, eXTP, THESEUS, HERMES and GIFTS. We conduct data analysis and modelling of multi-wavelength observations of galactic compact sources
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working time models and the possibility of mobile working, because creative and innovative ideas can arise anywhere. We also attach great importance to intensive induction training and an open and collegial
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. We create a work-life-balance through flexible working time models and the possibility of mobile working, because creative and innovative ideas can arise anywhere. We also attach great importance
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working time models and the possibility of mobile working, because creative and innovative ideas can arise anywhere. We also attach great importance to intensive induction training and an open and collegial
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modelling. The PhD programme is thus an excellent starting platform for a successful career in a field related to global biogeochemical cycles and Earth System Science. To answer questions relating to global
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to transferring research results into practice. LogDynamics collaborates closely with enterprises, especially in the logistics and production industry. The IGS aims to identify, describe, and model the intrinsic
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animal models to neuroimaging and computational approaches. Research in a translational setting In addition to traditional PhD positions, IMPRS-TP offers a unique integrated PhD/residence program in
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deforestation rates, improved agricultural practices, reduced mismanagement of land use and enhanced community engagement #assist in the biophysical modelling system (link to ClimateBridge cluster) to local